Presently, smart sensors ensure commercial decisions where integrated electronic systems can be securely organized using blockchain and quantum computing because of their unique characteristics and features. In the current scenario, large-scale quantum computers can be built in which most current cryptographic systems can be hacked. Since digital and quantum computers can conduct computations simultaneously, a quantum tool for blockchain framework design is required. Based on these concerns in this research, an enhanced quantum-assisted blockchain security model using the artificial intelligence (EQ-BSM-AI) technique has been proposed. This model validates cryptosystems and blockchain technologies to determine their vulnerability to quantum attacks. Further, in this model, quantum assisted edge computing technique has been used to model the Human-centric Internet of Things (HIoT) system by introducing a quantum key generation process. Based on the post-quantum blockchain (PQB), a secured cryptosystem that is highly resistant to quantum computer attacks has been introduced in this research. This quantum channel with multiple inputs and outputs (MIMO) is designed for a quantum-based communication system to make this model more efficient and withstand errors. In EQ-BSM-AI, an improved quantum encryption algorithm (IQEA) stores the keys for encryption with a generalized probability accumulation model. For the current quantum computers and communications, our proposed system resulted in an improved sampling error reduction of 12.4%, enhanced efficiency of quantum entanglement of 96.3%, information randomness of 93.9%, correlation analysis of 93.2%, and increased resistance to quantum computing attacks of 90.8% when compared with other existing approaches.
Presently, smart sensors ensure commercial decisions where integrated electronic systems can be securely organized using blockchain and quantum computing because of their unique characteristics and features. In the current scenario, large-scale quantum computers can be built in which most current cryptographic systems can be hacked. Since digital and quantum computers can conduct computations simultaneously, a quantum tool for blockchain framework design is required. Based on these concerns in this research, an enhanced quantum-assisted blockchain security model using the artificial intelligence (EQ-BSM-AI) technique has been proposed. This model validates cryptosystems and blockchain technologies to determine their vulnerability to quantum attacks. Further, in this model, quantum assisted edge computing technique has been used to model the Human-centric Internet of Things (HIoT) system by introducing a quantum key generation process. Based on the post-quantum blockchain (PQB), a secured cryptosystem that is highly resistant to quantum computer attacks has been introduced in this research. This quantum channel with multiple inputs and outputs (MIMO) is designed for a quantum-based communication system to make this model more efficient and withstand errors. In EQ-BSM-AI, an improved quantum encryption algorithm (IQEA) stores the keys for encryption with a generalized probability accumulation model. For the current quantum computers and communications, our proposed system resulted in an improved sampling error reduction of 12.4%, enhanced efficiency of quantum entanglement of 96.3%, information randomness of 93.9%, correlation analysis of 93.2%, and increased resistance to quantum computing attacks of 90.8% when compared with other existing approaches.
The convergence of blockchain technology and quantum-resistant artificial intelligence (AI) frameworks marks a significant advancement in cybersecurity. A blockchain-powered framework for quantum defensive AI aims to create robust, tamper-proof AI systems capable of withstanding the emerging threats posed by quantum computing. As quantum computers have the potential to break conventional cryptographic algorithms, integrating quantum-resistant cryptographic methods within a blockchain ensures data integrity and security. Due to being decentralized, blockchain makes it possible to see a ledger that is impossible to alter, which is required for monitoring AI actions and securing AI-powered applications. Here, blockchain is used so that data and models can be verified, their updates are secure, and everything can be audited. Machine learning is introduced to blockchain security to ensure that it remains protected from future quantum-style hacking attempts. Because of this integration, AI systems will be more secure, and the blockchain infrastructure will be better able to resist any future threats brought by quantum computers. It has been revealed by using blockchain technology with period defensive AI that both traditional and post-quantum transactions become more abundant with the passage of time. Out of every five transactions performed per second on a post-quantum computer, at least four utilize quantum principles and operate with much higher efficiency. This research is necessary to speed up the handling of transactions that need rapid attention. The choice of using a hybrid approach is sure to initiate a time when AI is highly secure and transparent, and it will be crucial in areas such as finance, healthcare, and the security of nations. Using these advanced technologies, it ensures that AI applications can remain stable against cyber threats that may exist in the future.
The integration of blockchain and artificial intelligence (AI) has revolutionized secure, transparent, and decentralized applications. However, the security of blockchain-based AI models remains reliant on classical cryptographic techniques, such as RSA and ECC, which are increasingly vulnerable to emerging quantum computing threats. This study investigates how quantum cryptographic techniques can enhance the security and resilience of blockchain-based AI applications. Specifically, it explores the role of Quantum Key Distribution (QKD) in securing key exchanges, post-quantum cryptographic (PQC) algorithms in fortifying data encryption, and quantum hashing techniques in protecting blockchain consensus mechanisms. The research evaluates the feasibility, implementation challenges, and performance implications of quantum-enhanced security frameworks for AI-driven blockchain networks. By addressing these concerns, this study establishes a foundation for developing quantum-secured blockchain infrastructures that safeguard AI transactions against future quantum threats while ensuring trust, transparency, and scalability in decentralized applications.
Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions.
The integration of quantum computing with blockchain technology has the potential to revolutionize the landscape of decentralized systems. This chapter discusses the transformative synergy between these technologies, with a focus on its implications for next-generation artificial intelligence (AI) applications within the blockchain domain. As blockchain continues to evolve as a trusted and immutable ledger, the deployment of quantum computing holds promises for enhancing security, scalability, and the capabilities of AI-driven smart contracts and consensus mechanisms. This work discusses the core principles of quantum computing and their applicability to blockchain technology. Quantum bits (qubits) can significantly enhance the cryptographic and consensus aspects of blockchain, making it more robust against traditional attacks, such as those related to public key cryptography (PKI). Furthermore, this work discusses the role of quantum-enhanced AI within the blockchain ecosystem. The integration of quantum machine learning and quantum neural networks into smart contracts and consensus algorithms opens new possibilities for improving decision-making processes, enhancing data analytics, and enabling predictive AI models on the blockchain.
This paper presents a comprehensive examination of post-quantum cryptographic techniques, also known as quantum encryption, which offer protection against quantum attacks. It explores various methods such as hash-based cryptography and lattice-based cryptography that can safeguard cryptographic hashing and encryption against the potential vulnerabilities posed by quantum computers. The focus is specifically on the implications for blockchain technology, as it is susceptible to compromise during quantum attacks. The study provides an overview of quantum cryptographic encryption techniques that are poised to withstand quantum attacks, ensuring the resilience and quantum-proof nature of cryptographic measures. Blockchain is a peer to peer network that maintains distributed ledger. The peers in the network shares information in the network that is secured using typical cryptographic mechanisms. The proposed system aims at integrating quantum resistant cryptographic techniques in the blockchain network which ensures enhanced security to the information shared in the network. The proposed algorithm improves level of security to the typical blockchain network.
Nowadays, blockchain has become one of the most cutting-edge technologies, which has been widely concerned and researched. However, the quantum computing attack seriously threatens the security of blockchain, and related research is still less. Targeting at this issue, in this paper, we present the definition of post-quantum blockchain (PQB) and propose a secure cryptocurrency scheme based on PQB, which can resist quantum computing attacks. First, we propose a signature scheme based on lattice problem. We use lattice basis delegation algorithm to generate secret keys with selecting a random value, and sign message by preimage sampling algorithm. In addition, we design the first-signature and last-signature in our scheme, which are defined as double-signature. It is used to reduce the correlation between the message and the signature. Second, by combining the proposed signature scheme with blockchain, we construct the PQB and propose this cryptocurrency scheme. Its security can be reduced to the lattice short integer solution (SIS) problem. At last, through our analysis, the proposed cryptocurrency scheme is able to resist the quantum computing attack and its signature satisfies correctness and one-more unforgeability under the lattice SIS assumption. Furthermore, compared with previous signature schemes, the sizes of signature and secret keys are relatively shorter than that of others, which can decrease the computational complexity. These make our cryptocurrency scheme more secure and efficient.